204 citations · 383 across the 11 of their papers we have counts for
13 papers
Population-Level Decision Curve Analysis May Mislead the Evaluation of Prediction Model Usefulness under Subgroup Utility Heterogeneity
Junfeng Wang, Kim Zhipei Wang, Ben Van Calster +3
Background Decision curve analysis (DCA) evaluates prediction model usefulness using net benefit (NB). Population-level NB is often interpreted as a proxy for population-level expe…
Optimizing for the decision not the prediction: an exploration of Smooth Net Benefit as a training objective
Koen M. F. Gorgels, Lasai Barreñada, Maarten van Smeden +3
Objective Prediction models are commonly trained using objectives such as Bernoulli negative log-likelihood (NLL), although downstream clinical decisions may depend on specific ris…
Collapsibility of Performance Metrics in Clinical Predictive AI
João Matos, Ben Van Calster, Richard D. Riley +2
Background: Population level assessments of predictive artificial intelligence (AI) can conceal performance disparities across subgroups. Fairness evaluations commonly rely on perf…
Efficient Variance Estimation for the Polytomous Discrimination Index
Qunqiang Feng, Taozheng Jia, Pan Liu +2
Evaluating diagnostic accuracy for multi-category outcomes remains a significant challenge, primarily due to computational limitations in existing performance metrics. The Polytomo…
Value-of-Information Analysis for External Validation of Risk Prediction Models in Multicenter Studies and Systematic Reviews
Laure Wynants, Kim Zhipei Wang, Sabine Grimm +5
External validation studies have finite sample sizes, creating uncertainty about whether a prediction model's Net Benefit (NB) exceeds default strategies' NB. The expected value of…
The fundamental problem of risk prediction for individuals: health AI, uncertainty, and personalized medicine
Lasai Barreñada, Ewout W Steyerberg, Dirk Timmerman +3
Background and Objective: Clinical prediction models are commonly evaluated regarding performance for a population, although decisions are made for individuals. The classic view re…
missForestPredict -- Missing data imputation for prediction settings
Elena Albu, Shan Gao, Laure Wynants +1
Prediction models are used to predict an outcome based on input variables. Missing data in input variables often occurs at model development and at prediction time. The missForestP…
Comparison of static and dynamic random forests models for EHR data in the presence of competing risks: predicting central line-associated bloodstream infection
Elena Albu, Shan Gao, Pieter Stijnen +6
Prognostic outcomes related to hospital admissions typically do not suffer from censoring, and can be modeled either categorically or as time-to-event. Competing events are common…
Understanding overfitting in random forest for probability estimation: a visualization and simulation study
Lasai Barreñada, Paula Dhiman, Dirk Timmerman +2
Random forests have become popular for clinical risk prediction modelling. In a case study on predicting ovarian malignancy, we observed training c-statistics close to 1. Although…
How to develop, externally validate, and update multinomial prediction models
Celina K Gehringer, Glen P Martin, Ben Van Calster +3
Multinomial prediction models (MPMs) have a range of potential applications across healthcare where the primary outcome of interest has multiple nominal or ordinal categories. Howe…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
Minimum Sample Size for Developing a Multivariable Prediction Model using Multinomial Logistic Regression
Alexander Pate, Richard D Riley, Gary S Collins +4
Multinomial logistic regression models allow one to predict the risk of a categorical outcome with more than 2 categories. When developing such a model, researchers should ensure t…
Risk prediction models for discrete ordinal outcomes: calibration and the impact of the proportional odds assumption
Michael Edlinger, Maarten van Smeden, Hannes F Alber +2
Calibration is a vital aspect of the performance of risk prediction models, but research in the context of ordinal outcomes is scarce. This study compared calibration measures for…